Session Information
10 SES 01 B, Preparing Teachers for Inclusive and Digital Education
Paper Session
Contribution
The current landscape of educational research is situated within what Morin (2020) defines as a “poly-crisis.” This term refers to a systemic and simultaneous entanglement of multiple emergencies affecting our society on a global scale: from post-pandemic fragilities to geopolitical instabilities, up to the climate crisis, which calls for a rethinking of collective life. This complexity does not operate in isolation; rather, it reverberates across the different levels of the educational ecosystem described by Bronfenbrenner (2023). Such pressure drives institutions toward a progressive datafication of knowledge, in which learning is often reduced to measurable units (Williamson, 2017; Williamson et al., 2020). Within this context, the education of future teachers enrolled in the Degree Programme in Primary Education Sciences now stands at a crucial crossroads. On the one hand, there is a push toward performative standardisation, oriented toward the management of large cohorts through numerical indicators and automated testing. On the other hand, there emerges the need to preserve academic sincerity. This crisis of sincerity reflects the tension between the automation of assessment, represented by Computer-Based Assessment (CBA), and the need for knowledge that remains situated, authentic, and deeply critical. If, as Biesta (2021) argues, the essence of education lies in “subjectification” and in the recognition of students’ humanity, then summative assessment cannot - and must not -be reduced to a mere act of algorithmic or statistical measurement.
In the era of generative Artificial Intelligence (AI) and the proliferation of fake news (Al-Rawahi & Al-Shammari, 2024; Drouiche, 2025), the central challenge of teaching professionalism lies in the ability to distinguish between the mere mechanical reproduction of data and the genuine acquisition of reflective competence (Fishman & Gardner, 2022). Within this framework, the present contribution proposes to analyse the oral examination not as an obsolete legacy of the past, nor as a condemnation of structured testing. On the contrary, objective tests are acknowledged as fundamental tools for ensuring standards of accountability, equity, and uniformity in the assessment of broad domains of knowledge. However, the oral examination is here investigated as a device of pedagogical resilience, necessary to complete and enrich the overall assessment profile.
While structured tests ensure bureaucratic efficiency and psychometric validity across extensive content areas, the oral examination functions as a glocal response (Sepúlveda Alzate, 1999). It represents a practice rooted in a solid local tradition that offers a macro-level solution to the fragmentation of the educational relationship. Indeed, evaluative dialogue allows for the emergence of that “professional knowledge” which inevitably eludes the binary cataloguing of Big Data: namely, critical argumentation skills, ethical stance, and the communicative competence of the future teacher. The oral examination therefore does not seek to replace objective tools but rather to integrate their epistemological limits, fostering a human encounter that takes shape as an act of institutional trust. From this perspective, the social responsibility of the researcher transcends the mere academic perimeter to take on the character of a conscious political action (Biesta, 2021). Documenting and defending the validity of dialogic assessment models through the analysis of Syllabi means resisting the drift toward a purely technocratic and faceless form of evaluation. In this view, the Syllabus ceases to be a cold bureaucratic requirement and becomes a manifesto of transparency. It turns into a genuine social contract that safeguards research autonomy and ensures depth of learning even in times of extreme uncertainty (Fullan et al., 2023). In conclusion, this study aims to demonstrate that safeguarding the oral assessment represents a forward-looking strategy. Such an approach is capable of ensuring the holistic quality of teacher education by integrating the effectiveness of large-scale.
Method
The research design adopts a mixed-methods approach. The central aim of the study is to investigate whether the oral examination, in its glocal dimension, can represent an effective micro-pedagogical response to the macro-European challenge of datafication. The study addresses two research questions: (1) to what extent does the oral examination function as a resilience device capable of ensuring the holistic integrity of teacher education; and (2) how does the Syllabus manage the tension between bureaucratic transparency requirements and the need to preserve pedagogical dialogue? The sampling strategy is systemic and includes the entire population of the 41 single-cycle master’s degree programmes in Primary Education Sciences offered in Italy during the academic year 2024–2025. Syllabi are adopted as the primary data source and are interpreted as indicators of the pedagogical intentions expressed by academic staff. The research is structured into three sequential phases. First, a systematic census of the declared assessment modalities is conducted, mapping the distribution of oral examinations, structured written tests, and Computer-Based Assessment (CBA). These modalities are analysed in relation to Messick’s (1995) validity criteria, with particular attention to the coherence between assessment methods and intended learning outcomes. Second, a comparative analysis of the assessment paradigms underlying the Syllabus descriptions is carried out. This phase draws on Broadfoot’s (2021) critical perspective on the risks of exclusively quantitative assessment, as well as on the framework of Sustainable Assessment, which emphasises the role of assessment in fostering students’ long-term capacity for autonomous judgement (Beck et al., 2013; Bearman et al., 2023). Third, Qualitative Content Analysis (QCA) is employed to examine the academic language used in the description of assessment practices (Schreirer, 2012). Within this phase, the Syllabus is conceptualised as a “boundary work” device (Gieryn, 1983), aimed at defining and protecting the boundaries of academic sincerity, particularly in relation to the misuse of generative artificial intelligence. Research rigour is ensured through a multi-level triangulation strategy. Methodological triangulation (Denzin, 2012) integrates quantitative data from the national census with qualitative semantic analysis of the Syllabi, while theoretical triangulation combines insights from docimology and the relevant scholarly literature (Iannotta & Tammaro, 2025).
Expected Outcomes
The analysis of the 41-degree programs in Primary Education Sciences within the Italian academic system yields conclusions that extend beyond a national perspective. The findings indicate that the persistence of the oral examination—adopted by more than half of Italian universities—should not be interpreted as a methodological anomaly or as passive adherence to tradition. Rather, in the context of the contemporary poly-crisis, it emerges as a form of conscious pedagogical resilience aligned with advanced European assessment perspectives. Although the Finnish model is often cited as a benchmark for high-trust cultures and assessment autonomy, this study suggests that the Italian system offers a procedurally robust articulation of these principles. In an era of increasing automation and a crisis of sincerity in written academic work, the oral examination fulfils an irreplaceable epistemological function by assessing dimensions that Big Data and Computer-Based Assessment (CBA) cannot capture, particularly pedagogical judgement in complex and uncertain contexts (Shermis & Burstein, 2013). The evidence highlights evaluative dialogue as a privileged means of exploring students’ reflective maturity, understood as the integration of theoretical knowledge, situated sensitivity, and ethical positioning. In line with Biesta (2021), assessment thus becomes an act of “subjectification,” recognising the emergence of professional identity. Finally, the study supports hybrid and sustainable assessment models that balance technological efficiency with educational relationships (Bearman et al., 2023), reframing assessment as an act of care for teaching professionalism (Sepúlveda Alzate, 1999).
References
Al-Rawahi, N., & Al-Shammari, S. (2024). Artificial Intelligence in Education: A Systematic Review. International Journal of Educational Research and Technology, 3(2), 1-15. Bearman, M., Dawson, P., & Boud, D. (2023). Reimagining Assessment in a Digital World. Springer. Beck, R. J., Skinner, W. F., & Schwabrow, L. A. (2013), A Study of Sustainable Assessment Theory in Higher Education Tutorials. Assessment & Evaluation in Higher Education, 38(3), 326-348. Biesta, G. (2021). World-centred education. Routledge. Broadfoot, P. (2021). The Sociology of Assessment: Comparative and Policy Perspectives. Routledge. Bronfenbrenner, U. (2023). The Ecology of Human Development. Harvard University Press. Denzin, N. K. (2012). Triangulation 2.0. Journal of Mixed Methods Research, 6(2), 80-88. https://psycnet.apa.org/doi/10.1177/1558689812437186 Drouiche, A. (2025). L’intelligenza artificiale come alleata della didattica: opportunità e limiti pedagogici. El-Tawassol, 5(31), 10-22. Fischman, W., & Gardner, H. (2022). The Real World of College. The MIT Press. Fullan, M., Azorín, C., Harris, A., & Jones, M. (2023). Artificial intelligence and school leadership: Challenges, opportunities and implications. School Leadership and Management, 44(4), 339-346. https://doi.org/10.1080/13632434.2023.2246856 Gieryn, T. F. (1983). Boundary-Work and the Demarcation of Science from Non-science: Strains and Interests in Professional Ideologies of Scientists. American Sociological Review, 48(6), 781-795. Iannotta, I. S., & Tammaro, R. (2025). Uno studio esplorativo sulle pratiche di valutazione sommativa all’Università. Pedagogia oggi, 23(1), 137-143. Messick, S. (1995). Validity of psychological assessment. Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741-749. https://doi.org/10.1037/0003-066X.50.9.741 Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007 Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage. Sepùlveda Alzate, J. (1999). Pedagogìa glocal: un anàlisis desde el enfoque de las capacidades. Revista Repide, 9(9), 57-64. Shermis, M. D., & Burstein, J. (2013). Handbook of automated essay evaluation: Current applications and new directions. Routledge. Soumia, B. (2024). La valutazione sommativa di tipo soggettivo. Una valutazione adeguata all’approccio per competenze. Aleph, 11(1), 119-130. https://asjp.cerist.dz/en/article/240842 Williamson, B. (2017). Big Data in Education: The digital future of learning, policy and practice. Sage Publications. Williamson, B., Bayne, S., & Shay, S. (2020). The Datafication of Teaching in Higher Education: Critical Issues and Perspectives. Teaching in Higher Education, 25(4), 351-365. https://doi.org/10.1080/13562517.2020.1748811
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